Where do the antibiotic resistance genes come from? A modulated analysis of sources and loads of resistances in Lake Maggiore

Abstract Antibiotic resistance genes (ARGs) are abundant in aquatic ecosystems affected by human activities. Understanding the fate of ARGs across different ecosystems is essential because of the significant role aquatic environments play in the cycle of antibiotic resistance. We quantified selected ARGs in Lake Maggiore, its main tributaries, and the effluent of the main wastewater treatment plant (WWTP) discharging directly into the lake. We linked their dynamics to the different anthropogenic impacts in each tributary's watershed. The dynamics of tetA in the lake were influenced by those of the rivers and the WWTP effluent, and by the concentration of N-NH4, related to anthropogenic pollution, while sul2 abundance in the lake was not influenced by any water inflow. The dynamics of the different ARGs varied across the different rivers. Rivers with watersheds characterized by high population density, touristic activities, and secondary industries released more ARGs, while ermB correlated with higher numbers of primary industries. This study suggests a limited contribution of treated wastewater in the spread of ARGs, indicating as prevalent origin other sources of pollution, calling for a reconsideration on what are considered the major sources of ARGs into the environment.


Introduction
Antibiotic resistance genes (ARGs) are widespread in aquatic environments, and their abundance and richness are larger when anthropogenic pollution is high (Zhu et al. 2017 ).The role the environment plays in the spread and in the transmission of antibioticresistant pathogenic bacteria and of ARGs has been widely documented (Larsson et al. 2018 ), but a number of questions remain open about their dynamics and the factors underlying their ecological success in open waters.Among the different aquatic ecosystems, large lakes in areas exposed to anthropogenic pressur e ar e an important natur al r eservoir of ARGs (Ec kert et al. 2018 , Nnadozie andOdume 2019 ).Resistances r eac h lakes mainly from their watershed, brought from the tributary rivers, which are collecting ARGs from diffuse (water runoff fr om a gricultur al and industrial ar eas, r oads, cities and leisur e ar eas) and/or punctual sources [effluent of wastewater treatment plants (WWTPs), se wa ge bypasses, untr eated disc har ges] (Pruden et al. 2012, Marti et al. 2014 ).
No w ada ys , the general consensus is that WWTP effluents are identified as a major source of ARGs into surface waters (Rizzo et al. 2013, Czekalski et al. 2014 ), and a large body of r esearc h unv eils the impact of WWTP effluents on the micr obial comm unities of the r eceiving envir onments, the fate of the ARGs within the natur al comm unities, and the r emov al efficiencies of WWTPs a ppl ying differ ent tec hnologies (Czekalski et al. 2014, 2016, Corno et al. 2019, Sivalingam et al. 2023 ).The impact of diffuse ARG sources is also well documented, but the interactions between structured envir onments (complex anthr opogenicall y impacted ar eas, riv ers, lakes) coupled with the impact of climatic and meteorological par ameters, r educe our understanding of the impact caused by these sources on the spread of the ARGs (Di Cesare et al. 2017, Yu et al. 2018 ).
In this study, we investigated the dynamics of five ARGs in the watershed of Lake Ma ggior e, a deep oligotr ophic subalpine lake shared between Italy and Switzerland (lake surface: 212 km 2 , max depth: 370 m).This lake is one of the most studied lakes in the world and it r eceiv es waters fr om one of the largest catchment areas (6659 km 2 ) in the Alpine Region, shared almost equally between Italy and Switzerland.This area hosts about 640000 inhabitants (CIPAIS 2003 ) and a large number of seasonal tourists, mainly in spring and summer (the number of tourists hosted in the watershed of the Lake in 2015-6 was between 4 and 5 million; UST A T Canton Ticino 2017 , IST A T 2017a ).From 2013, the horizontal and vertical distribution of ARGs in Lake Ma ggior e has been assessed monthly by monitoring programs funded by the International Commission for the Protection of Italian-Swiss Waters (Di Cesare et al. 2015, CIPAIS 2018, Eckert et al. 2019a, Di Cesare et al. 2020, CIPAIS 2021 ).Ho w e v er, the r elativ e contribution of the ARGs r eleased fr om its watershed thr ough the tributary riv ers or from the WWTP effluents directly discharged into the lake to the resistome of the lake has never been investigated.We sampled the lake, its six main tributaries (i.e.Bardello, Strona, San Bernardino, Ticino, Toce, and Tresa), and the effluent of the WWTP located in Verbania, one of the largest (and the few) WWTPs disc har ging dir ectl y in the lake , o ver the course of one year: ov er all we anal yzed samples cov ering ∼80% of the annual water inflow of Lake Ma ggior e .T he Str ona and Toce riv ers, gathering waters fr om watersheds with different human use, were sampled shortly before the former flows into the latter, at a short distance from the Toce estuary into Lake Ma ggior e.For all sampling sites, we quantified five ARGs (present on the bacterial chromosome or within plasmids) by means of quantitative real time PCR (qPCR).The selection of ARGs was guided by the results obtained by the CIPAIS long-term monitoring of Lake Ma ggior e, focusing on genes associated with resistance to the most commonly used antibiotics in the Lake Ma ggior e ar ea (CIPAIS 2018 ).Two genes, sul 2 and tet A (encoding for the resistance to sulphonamides and tetracyclines, r espectiv el y) wer e almost al ways pr esent in the lake (Di Cesare et al. 2015 ); bla CTX-M (encoding for resistance to β-lactams) follo w ed a seasonal dynamic within the lake bacterial community (Eckert et al. 2019a ); erm B and qnr S (encoding for the resistance to macr olide-lincosamide-str eptogr amin and quinolones, r espectiv el y) wer e spor adicall y detected in the lake, and are classified as high-risk ARGs for human health (Zhang et al. 2021 ).We also measured biotic and abiotic factors that may influence the bacterial community and their associated ARGs: bacterial cell number and size distribution, water flow, nitrogen and phosphorus compounds and total organic carbon.We then analyzed results taking into consider ation the le v el and the type of anthr opogenic pr essur es (urban, a gricultur al, industrial, touristic) impacting in each tributary's watershed.For this reason, we collected specific data (number and density) on resident population, primary and secondary industries , hotels , as well as the number and size of WWTPs in the basin of the lake.Finally, using the WWTP of Verbania as a model, we a ppr oximated the impact of treated effluents in the different watersheds and dir ectl y into the lake.

Site sampling, water collection and processing
We collected composite (0-20 m) water samples ( > 5 L) from Lake Ma ggior e at the CIPIAS sampling site of Ghiffa [WGS84 latitude (N) (Di Cesare et al. 2016a, CIPAIS 2018 ).We sampled the sites e v ery two months fr om Jul y 2015 to May 2016.An aliquot of water was pr efilter ed on a 126-μm mesh size net and 1.5 ml of filtered w ater w as fixed with formalin (1% final concentration) to be analyzed by flow cytometry.An aliquot (1 L) was processed to measure the main chemical variables.Another aliquot was pr efilter ed on a 10-μm mesh size net to r emov e lar ge particles and metazoan and a known volume of filtered water (between 100 ml and 1 L) was further filtered onto 0.22-μm polycarbonate filters and stored at -20 • C for the molecular analyses.
Eac h riv er's disc har ge and the corr esponding watershed ar ea wer e obtained fr om the CIPAIS Ann ual Re port 2017 (CIPAIS 2018 ); population, number of hotels, number of primary (i.e.producing raw materials like farms and caves) and of secondary industries (i.e.manufacturers) in each watershed were obtained from the Ann ual Re ports of the Italian Institute of Statistics (IST A T 2017a ,b ) for the Italian part of the lake basin, and from the Report by the Dipartimento del Territorio del Canton Ticino ( 2018 ) for the Swiss part of the basin ( Table S1 ).

Chemical variables and bacterial abundance measurements
We measured total organic carbon (TOC), total nitrogen (TN), nitrate (N-NO 3 ), ammonium (N-NH 4 ), total phosphorus (TP) and r eactiv e phosphorus (RP) as pr e viousl y described (Sathicq et al. 2022 ) and according to standard methods for freshwater samples (APHA/A WW A/WEF 2012 ).We analyzed the bacterial abundance and size distribution b y flo w c ytometry (BD Accuri C6) after staining of the water samples with SYBR Green I (final concentration 1%, Life Technologies).We quantified single bacterial cells, microcolonies (up to 10 cells) and bacterial a ggr egates ( > 10 a ggr egated cells); the limits of the counts and the gates in the cytograms were determined as previously described (Corno et al. 2013 ).The identification of these three gates allows the comparison of micr obial comm unities wher e the fr ee-living life style is prominent fr om those wher e a ggr egates and particle-attac hed bacteria ar e more abundant.An increase in aggregational forms, when supported by other chemical and physical data, suggests environment more disturbed, either by ecological (Callieri et al. 2016 ) or anthr opogenic (Ec kert et al. 2019b ) str essors.

DN A extr action and 16S rRN A gene and ARG quantification
Two quarters of each filter (see section Site sampling, water collection and processing) were processed for DNA extraction using the commercial kit DNeasy UltraClean (Qiagen), following the manufactur er's instructions.Eac h DN A sample w as 2-fold diluted and analyzed to quantify the genes 16S rRNA, sul 2, tet A, bla CTX-M , erm B and qnr S by qPCR using primers, protocols and programs as previously published (Di Cesare et al. 2015 ).The potential PCR inhibition was tested by the dilution method as pr e viousl y described (Di Cesare et al. 2013 ) and no inhibition was determined.The efficiency of reaction, R 2 and the limits of quantification were measur ed as pr e viousl y described (Bustin et al. 2009 ).T he a v er a ge value ± standard deviation of the efficiency of reaction and R 2 considering all the reactions performed were 95.56 ± 16% and 0.98 ± 0.01, r espectiv el y.The limits of quantification (LOQ) were 57, 41, 11, 73, 54 and 62 copy/ μL for the 16S rRNA, sul 2, tet A, bla CTX-M , erm B and qnr S genes, r espectiv el y.The gene abundance was expressed conv erting ng/r eaction in gene copy/reaction as pr e viousl y shown (Di Cesare et al. 2013 ).The ARG abundances wer e expr essed in r elativ e manner dividing the copy number of ARG per the copy number of the 16S rRNA gene.In cases where the value of the abundance of each gene was below the LOQ but higher than three copy/reaction (the theoretical limit of qPCR, Bustin et al. 2009 ), the gene was considered present but not quantifiable (NQ).The treating of the values of the gene abundances between the two replicates per sample was done as previously described (Di Cesare
First, w e used P earson's r corr elations to explor e, acr oss sampling dates (n = 6), the correlation between the abundance of the genes in the tributary rivers and in Lake Ma ggior e.
Next, w e ran tw o sets of generalized linear mixed models to explore the effect on the presence/absence (first set of models) or abundance (second set of models) of the five genes as response variable of the biotic and abiotic variables of the water in each sampling site/date (total cell n umber, n umber of a ggr egates, number of microcolonies, N-NO 3 , N-NH 4 , TN, TOC, RP and TP) as predictors.We constructed r egr ession models acr oss all sites and sampling dates, with a total sample size of 52 observations.In regr ession anal yses, w e follo w ed the general protocol from Zuur and Ieno ( 2016 ).
Prior to model construction, we visually inspected variable distribution, pr esence of outliers, m ulticollinearity among pr edictors (using pairwise Pearson's r correlations) and balance of factor levels (Zuur et al. 2010 ).As a result of multicollinearity analysis, we k e pt as contin uous inde pendent variables total cell number, TN, N-NH 4 , TP and sampling date (all having pairwise | r | proxy of load of < 0.7), while we excluded the number of a ggr egates, number of microcolonies, N-NO 3 , TOC and RP) ( Fig. S1 ).Also, we scaled to a mean of zero and a standard deviation of one all continuous independent variables to obtain comparable effect sizes and facilitate conv er gence of r egr ession models.
We fitted models using the R pac ka ge "glmmTMB" version 1.1.1(Brooks et al. 2017 ).For the presence/absence models, we assumed a Bernoulli distribution and a complementary log-log link function (clog-log), as is recommended for unbalanced distributions between zeros and ones (Zuur et al. 2009 ).For the concentration models, we assumed a Gaussian distribution.The structure of the models, in R notation, was: y ∼ sampling date + total cell number where y (dependent variable) is the presence/absence or abundance of the five genes.Note that, because of the high pr e v alence of zeros, we could not run the abundance model for the bla CTX-M gene, resulting in a total of nine individual models.In all models, we included sampling site as a random intercept structure (factor with eight le v els) to account for the non-independence of samples (i.e.pseudo-replication stemming from repeated measures at each site).We carried out model validation by inspecting model residuals with the check_model function in the package "performance" version 0.9.0.6 (Lüdecke et al. 2021 ).
Finall y, we explor ed the r ole of differ ent geomor phological and anthropogenic factors of each tributary's watershed in driving the abundance of ARGs.For this, we selected eight catc hment-le v el v ariables: bacterial a ggr egates, w aterflo w, catc hment ar ea, total population, number of primary industries (farms, mines), number of industries and number of hotels (a proxy for the touristic pr essur e).Using Pearson's r corr elation, we explor ed the corr elation between the abundance of the five ARGs genes and these eight variables.We also used principal component analysis (PCA) to reduce the dimensionality of this dataset and identify the underlying patterns in the relationships among variables.First, we standardized data to ensure that eac h v ariable had equal weight in the analysis.We used standardized variables to calculate the cov ariance matrix, whic h describes the r elationships between the variables; next, we computed the eigenvectors and eigenvalues of the covariance matrix and used the eigenvectors to transform the data into principal components.

Chemical parameters and bacterial abundance and size distribution
Apart for TOC and N-NH 4 that, as av er a ge v alues, wer e pr esent in higher concentrations in River Bardello (3.28 and 0.112 mg/L, respectiv el y), all the other measured chemical variables, that is, TN, N-NO 3 , TP and RP had higher concentrations in the effluent of the WWTP (6.33, 5.80, 0.536 and 0.468 mg/L, r espectiv el y) ( Table S2 ).
Ta ble 1. P earson's r correlation between the abundance of resistance genes ( sul 2 and tet A) measured in Lake Ma ggior e and in the rivers and WWTP effluent.River Bardello also showed the second highest concentration of TN, TP, N-NO 3 and RP.Bacterial cell numbers were higher in River Tresa, both as an average value (4.28 × 10 6 cell/mL) and as the highest concentration in a single sample (6.18 × 10 6 cell/mL).River Tresa was also the river with the highest number of microcolonies (1.44 × 10 6 micr ocolonies/mL).Aggr egates wer e instead higher in River Bardello (4.93 × 10 4 aggregates/mL) than in the other rivers ( Table S2 ).

Gene abundances
The sul 2 and tet A genes were present in all the samples from the WWTP effluent and often also in the lake and rivers.When quantifiable, sul 2 r anged fr om 5.91 ×10 −5 to 2.23 ×10 −2 gene copies/16S rRN A gene cop y and tet A from 1.08 ×10 −4 to 4.87 ×10 −2 gene copies/16S rRNA gene copy (Fig. 2 ).The bla CTX-M gene was ne v er detected in the samples collected from the WWTP effluent and fr om Riv er Toce, in the other samples it was onl y spor adicall y positiv e but not quantifiable (it was quantifiable in one sample from River Bardello: 5.54 ×10 −5 gene copies/16S rRNA gene copy) (Fig. 2 ).The erm B and qnr S genes were present in all the samples from the WWTP effluent and generally positive in the samples collected from the ri vers.In Lak e Maggiore these genes were, apart for one sample, never detectable.When quantifiable, erm B was comprised between 1.04 ×10 −4 and 3.08 ×10 −2 gene copies/16S rRNA gene copy and qnr S between 2.58 ×10 −4 and 6.70 ×10 −3 gene copies/16S rRNA gene copy (Fig. 2 ).

Correlation of the ARG copies/16S rRNA gene copy between Lake Maggiore and the rivers or the effluent of WWTP
Only two genes, sul 2 and tet A, were abundant enough to allow testing the correlation of their relative abundances between the data measured in Lake Maggiore and those in the rivers and in the effluents of the WWTP.The abundance of the sul 2 gene in Lake Ma ggior e was not corr elated with the one measur ed in the other sampled sites.By contrast, tet A measured in Lake Maggiore was correlated with the one measured in the rivers Bardello, San Bernardino, Ticino and Tresa (all Pearson's r ≥ 0.8) and with that determined in the effluents of the WWPT (all Pearson's r ≥ 0.87) (Table 1 ).

Rela tionship betw een biotic and abiotic factors and ARGs
The detection frequency of the erm B gene increased significantly with increasing values of N-NH 4 ( P = 0.008) and decreased with incr easing concentr ation of bacterial cell ( P = 0.013) (Fig. 3 ; Table S3 ).
The detection frequency of the bla CTX-M gene rose as the bacterial cell concentration increased ( P = 0.001) (Fig. 3 ; Table S3 ).For sul 2, tet A and qnr S genes, no significant effect of the tested biotic and abiotic factors on their presence was observed (Fig. 3 ; Table S3 ).
The abundances of the sul 2 and qnr S genes were positively associated with sampling date ( P < 0.001, P = 0.033, r espectiv el y) (Fig. 4 ) ( Table S4 ).The qnr S gene was also positiv el y influenced by TN ( P = 0.005) (Fig. 4 ) ( Table S4 ).The abundance of the tet A gene was positiv el y affected by N-NH 4 ( P = 0.002) (Fig. 4 ; Table S4 ).The abundance of erm B was not affected by any of the tested abiotic and biotic factors (Fig. 4 ; Table S4 ).

Influence of ca tchment-le vel predictors on ARGs
The abundances of ARGs in the Lake Ma ggior e and its tributaries correlated with land use and anthropogenic pressures (Fig. 1 , Fig. 5 A): while primary industries were not a major driver to define the different watersheds, secondary industries, resident population and touristic activities were impacting more in the watershed of the rivers Tresa and Bardello.Interestingly, while overall numbers and density rather correlated for population and secondary industries across the different river watersheds (high number and density for Bardello and Tr esa, low v alues for Toce and Ticino), the parameters for primary industries and touristic pressure (number of hotels and density) were not proportional among the wa-  2 .tersheds .T he rivers Tresa and Bardello were also the ones with the highest load of ARGs per cubic meter and, together with the River Toce, with the highest ov er all v alues per year.All the ARGs str ongl y corr elated with these par ameters (Fig. 5 B), while only erm B was correlated to the overall presence of primary industries.As a result, the overall ARG load from each watershed (Fig. 1 F) was concomitantly influenced by both, overall abundance and density of each pressure predictor, in a modulated way (Fig. 1 A-C and E).
Using the effluent of the WWTP of Verbania as proxy of load of ARGs in the effluents of the other WWTPs in the area (the differ ent WWTPs ar e r eceiving v ery compar able inflows, the le v el of treatment, although some differences, is comparable, as well as the limits they should meet according to the Italian and the Swiss laws on w astew ater effluent quality) we defined the ARG load fr om eac h lar ge and medium-sized WWTP (considering the w ater flo w of each single WWTP), and thus, within the ov er all load measured in each watershed (Fig. 1 D), we extr a polated the pr oportion of measur ed ARGs potentiall y originated fr om WWTP effluents .T hese values are generally limited ( < 10% of the overall load per year for eac h consider ed ARG; Fig. 6 ) in comparison with the ARGs from other sources of pollution.The proportion of the sul 2 gene originated from WWTP effluents is higher in all watersheds, but especially in River Ticino (75%), Tresa (44%) and Bardello (25%).A str ong pr e v alence of WWTP effluent-originated genes was detected also for erm B in the River Tresa, and for qnr S in the River Ticino.The ARGs directly discharged from WWTP effluents in Lake Ma ggior e include those fr om the Verbania WWTP (and a few smaller WWTPs) and represent a limited amount when compared with those discharged through the rivers (Fig. 6 ).Large and medium-sized WWTPs in the watershed of Lake Ma ggior e and their water flows are listed in Table S5 .

Discussion
The dynamics of the ARGs measured in Lake Ma ggior e ar e av ailable from 2013 (detected monthly) and are characterized by large differences among genes (Di Cesare et al. 2015, Eckert et al. 2019a, CIPAIS 2021 ): bacteria carrying sul 2 and tet A are constantly present and abundant in the waters of the lake in the last 10 years, bla CTX-M in 2015-6 was onl y spor adicall y quantifiable, missing the seasonality c har acterizing other years (pr esent in winter/spring, absent for the rest of the year; Eckert et al. 2019 ).T he sul 2 gene , widely distributed and abundant in the microbiome of the lake, was also disc har ged in lar ge numbers fr om the riv ers (mainl y fr om T oce, T resa and Bardello), but without an y corr elation between loads and abundances in the lake .T he occurrence and abundance of sul 2 in the lake suggest its constitutive presence in the lacustrine bacterial community (where this gene is always detectable, and it can spor adicall y equal the abundances of the 16SrRNA gene; Di Cesare et al. 2015 ) and, thus, a general independence of its dynamics from external loads .T he other tested genes resulted as gener all y absent (or non-quantifiable) in the lake, despite the important load we could detect from the watershed and the evidence of their wide distribution in freshwater ecosystems (Proia et al. 2016, Di Cesare et al. 2017, Wang et al. 2020 ).This shows that the micr obial comm unity of the lake is in some way resistant (or resilient) to the stabilization of bacteria carrying these genes, as demonstrated also by the bacterial community of Lake Ma ggior e  3 .at the estuary of Riv er Bardello, whic h is in fact not affected by the disc har ge of ARG-polluted waters from the river (Corno et al. 2023 ).In our article (Corno et al. 2023 ) we could demonstrate a clear filtering of most ARGs of anthropogenic origin identified in River Bardello, once its waters were entering Lake Maggiore .T his could be related to the limited water flow of the river, in comparison with the huge water mass of the lake (dilution effect), but also by the high selectiv e pr essur e oper ated b y the w ell-established micr obial comm unities of the lake a gainst the pr olifer ation of allochthonous bacteria (and genes) from the river.
In contrast to sul 2 and the other measured ARGs, a strong effect on the abundances of the tet A gene in the lacustrine bacterial community and in the rivers (and of the effluent of the WWTP) was detected.In fact, tet A (and also other ARGs) was positiv el y affected by ammonium (N-NH 4 ), a chemical compound generall y pr esent in lo w quantities in pristine w aters (Marañón et al. 2006 ) and showing higher concentrations in the presence of anthr opogenic pollution (e.g.waste waters, leac hates, runoff fr om waste disposal sites and a gricultur al fields, atmospheric deposition; summarized in Huang et al. 2018 ).The positiv e corr elation between the presence/abundance of several ARGs and the concentration of N-NH 4 supports the hypothesis that the anthropogenic contamination of water is one of the main factors explaining the spread of ARG-carrying bacteria.Other factors influencing ARG dynamics were sampling date (suggesting a potential seasonality), in addition to ov er all bacterial abundance and TN concentr ations, both par ameters that, in subalpine watersheds, can be related to anthropogenic impact.
To elaborate further, we observed that areas characterized by greater population, higher population density, industrial activity (such as secondary industries) and hotels had released high abundances of most ARGs into the lake .Con v ersel y, ar eas with high numbers of primary activities like a gricultur e and mining disc har ged lar ger amounts of the erm B gene into the lake .T his finding suggests that the land use can contribute to the dissemination of div erse antibiotic r esistances into the envir onment, e v en in ar eas like the basin of Lake Ma ggior e, wher e human activities ov erla p extensiv el y among the different tributary watersheds.
The (very) low concentrations of heavy metals in the water of Lake Ma ggior e (CIP AIS 2016, CIP AIS 2017) and of the tributaries, as well as the extr emel y low concentr ations of antibiotics and other pharmaceuticals [ov er all concentr ation ∼17 ng L −1 in Lake Maggior e; S. Castiglioni (personal comm unication)] suggest a limited dir ect selectiv e (or co-selectiv e) pr essur e to w ar ds the spr ead of r esistances, and possibly implies their allochthonous origin.
The different river catchments were not only exposed to a certain degree of different human activities but received the effluents of a number of large and medium-sized WWTPs ( > 10 000 PE), whic h ar e gener all y consider ed hotspots for the spread of ARGs into the environment (Rizzo et al. 2013 ).By using the measure- ments of the ARGs within the bacterial community of the WWTP effluent from Verbania as a model for the effluents in the area, adjusted on the r espectiv e w ater-flo w r ate of eac h WWTP effluent, we could a ppr oximate the impact of WWTP effluents on the overall ARG load in the bacterial communities of the different rivers.This was possible because, according to the data available on the effluents of other WWTPs in the area (Gravellona Toce, Cannobio , Bioggio , Giubiasco , Gavir ate), we had e vidence of a compar able load and distribution of ARGs in the different effluents (qPCR and shotgun metagenomic data from Corno et al. 2019, 2023, CIPAIS 2021 ).Sur prisingl y, with the exclusion of a few cases and, generally, of the sul 2 gene, the contribution of the WWTP effluents to the ov er all ARG numbers was extr emel y limited.This is in contrast to the gener all y accepted assumption that pr esents WWTP effluents among the most prominent sources of ARGs of anthropogenic origin into the environment (Karkman et al. 2018 ).The ov er all impact in terms of spread of ARG within the microbiome of the lake caused by WWTP effluents that disc har ge dir ectl y into the lake was also limited.This does not mean that WWTP effluents are not a hotspot for the selection of ARGs in potentially pathogenic bacteria, which has been well demonstrated to date (Proia et al. 2018, Bengtsson-Palme et al. 2019, Alexander et al. 2020 ), but rather that their impact as a source of antibiotic resistances in high-income countries with efficient WWTPs is limited both quantitativ el y and (pr obabl y) spatiall y, compar ed with other sources, whether they are diffuse, point sources, or occasional.
It is indeed possible that, especially in disturbed ecosystems (e.g.River Bardello), an immediate increment of ARGs from the WWTP effluents is promoted by the sudden growth of antibioticresistant bacteria released with the treated w astew ater (Di Cesare et al. 2023 ), but this situation is unlikely to happen in more complex and stable environments like River Toce and River Ticino, and e v en mor e so Riv er Tr esa, wher e the anthr opogenic impact fr om the upper part of the watershed is buffered by the presence of a deep large lake (Lake Lugano) and its structured and ecologicall y r esistant micr obial comm unity (Corno et al. 2019 ).Still, e v en for River Bardello, the estimated contribution in terms of ARGs by the two WWTP effluents dir ectl y disc har ging into the riv er to the ov er all ARGs number measured at the river's mouth in the lake is limited.
Our findings lend support to the findings presented by Lee et al. ( 2022 ) in Swiss riv ers, whic h indicated that the impact of untr eated se wa ge fr om waste water bypasses on the le v els of ARGs in the environment can be greater over the course of a year than the impact caused by treated effluents.Given that conventional w astew ater treatments can reduce ARGs by 80-99% (Di Cesare et al. 2016b, Sabri et al. 2020 ), it is plausible that heavy rainfall and subsequent activation of w ater b ypasses lasting 10-20 days in a year can cause a disc har ge into the environment of a similar amount of ARGs as the effluents of their otherwise receiving WWTPs.
Additionall y, our r esults highlight the direct contribution of water runoff fr om anthr opogenic activities such as agriculture, industry and roads to the presence of ARGs in the rivers .T his underscores the need for further research to quantify and identify the specific types of ARGs released by these activities, looking for a possible correlation between the fate of allochthonous resistant bacteria in lakes and rivers and the magnitude of water pollution, of anthropogenic pressures and, in general, to the ecological instability of any environment exposed to high levels of stress.
Ultimately, our findings suggest that the current use and design of water bypasses must be re-evaluated, and that WWTP effluents should no longer be considered as the primary, and sometimes the only, source of ARG release into surface waters.

Figure 1 .
Figure1.Anthr opogenic pr essur es in the differ ent watersheds in Lake Ma ggior e basin: (A) primary industries (a gricultur e, mining), (B) secondary industries (factories, transformation industries), (C) resident population, (D) large and medium-sized WWTPs, (E) hotels, r ecr eational structur es, (F) ov er all ARGs load from the watershed into the lake per year.The name of the r espectiv e riv ers in (A) refers to all panels .T he orange dots in (F) r epr esent the sampling points for each watershed (1: Ticino, 2: Tresa, 3: Toce, 4: WWTP effluent, 5: San Bernardino, 6: Strona, 7: Bardello).The red circle in (A), (B), (C), (E) and (F) r epr esents the effluent of the WWPT of Verbania and its r espectiv e contributions .T he numbers r efer to the absolute v alue for eac h par ameter in the r espectiv e watershed, while the color r efers to the density of eac h par ameter in eac h w atershed (bright: lo w er, dark: higher).In (D), red circles are proportional to the size of the WWTP and numbers represent the water flow of each WWTP effluent (in m 3 sec −1 ).

Figure 2 .
Figure 2. Abundances of the ARGs ( bla CTX-M , erm B, qnr S, sul 2, tet A).The mean gene abundances (normalized to 16S rRNA gene) measured in different sites and dates.NQ indicates samples that resulted positive but not quantifiable, NEG refer to samples where the gene was not detected.

Figure 3 .
Figure 3. Influence of abiotic and biotic factors on the probability of presence of the target ARGs.Forest plots summarize the estimated parameters based on Bernoulli generalized linear mixed models.Error bars mark confidence intervals.Asterisks mark significant effects ( * P < 0.05; * * P < 0.01).Estimated r egr ession par ameters and P v alues ar e in Supplementary Table2.

Figure 4 .
Figure 4. Influence of abiotic and biotic factors on the r elativ e abundance of the target ARGs (normalized to 16SrRNA gene).Forest plots summarize the estimated parameters based on linear mixed models.Error bars mark confidence intervals.Asterisks mark significant effects ( * P < 0.05; * * P < 0.01).Estimated r egr ession par ameters and P v alues ar e in Supplementary Table3.

Figure 5 .
Figure 5. Influence of catc hment-le v el pr edictors on the abundance of ARGs.(A) Bi-plot of principal component anal ysis (PCA) scor es for the first two axes based on the se v en catc hment-le v el v ariables .T he position of eac h site is marked with color ed dots.(B) Heat-ma p sho wing P earson's r correlations between the abundance of the five target genes and the seven catchment-level predictors.

Figure 6 .
Figure 6.Proportion of ARGs originated directly from WWTP effluents or from other sources .T he last panel refers to the number of ARGs released by the WWTP of Verbania, compared with those by other WWTPs directly discharging in Lake Maggiore .T he x-axis indicates the extrapolated value of ARGs per year in each sampling site.